Nowadays, renewable energy sources such as solar Photovoltaic (PV) and wind power systems play a significant part in the process of generating electricity. These systems are tremendously reliant on the present and forecasted weather conditions. The inconsistent behavior of the system has an impact on the production as well as the variances in the output. As a result, the requirement for energy transmission and distribution systems to compensate promptly is becoming crucial. This paper proposes a new smart grid application for power system operation. A Static Compensator (STATCOM) device is utilized to improve the power quality and power flow in the distribution system, reduce unwanted harmonics and compensate for reactive power in the power sources. A quasi-Z-Source Inverter (qZSI) based STATCOM is constructed in connection with a Three-Phase Four-Wire (3P4W) distribution system. The combination of a q Z-source and a Photovoltaic (PV) system makes up the suggested compensator circuit that is employed here for switching. To control the suggested compensator, an Adaptive Frequency Fixed (AFF) - Second Order Generalized Integrator (SOGI) has been implemented. The Fuzzy Logic Controller (FLC) is used to optimize the parameters of the Proportional Integral (PI) controller, such as Kp and Ki. The performance is evaluated using a variety of characteristics, including power, current, and voltage, generated reactive power and active power, analysis in non-linear load, and other similar metrics.

At present, renewable energy sources (RESs) and electric vehicles (EVs) are presented as viable solutions to reduce operation costs and lessen the negative environmental effects of microgrids (μGs). Thus, the rising demand for EV charging and storage systems coupled with the growing penetration of various RESs has generated new obstacles to the efficient operation and administration of these μGs. In this regard, this paper introduces a multi-objective optimization model for minimizing the total operation cost of the μG and its emissions, considering the effect of battery storage system (BSS) and EV charging station load. A day-ahead scheduling model is proposed for optimal energy management (EM) of the μG investigated, which comprises photovoltaics (PVs), fuel cells (FCs), wind turbines (WTs), BSSs, and EV charging stations, with shed light on the viability and benefits of connecting BSS with EV charging stations in the μG. Analyzing three case studies depending on the objective function—Case 1: execute EM to minimize total operation cost and maximize the profits of BSS, Case 2: execute EM to minimize total emission from the μG, and Case 3: execute EM to minimize total operation cost, maximize the profits of BSS, and minimize total emissions from the μG. The main aim of the presented optimization strategy is to achieve the best possible balance between reducing expenses and lessening the environmental impact of greenhouse gas emissions. The krill herd algorithm (KHA) is used to find the optimal solutions while considering various nonlinear constraints. To demonstrate the validity and effectiveness of the proposed solution, the study utilizes the KHA and compares the obtained results with those achieved by other optimization methods. It was demonstrated that such integration significantly enhances the μG’s operational efficiency, reduces operating costs, and minimizes environmental impact. The findings underscore the viability of combining EV charging infrastructure with renewable energy to meet the increasing energy demand sustainably. The novelty of this work lies in its multi-objective optimization approach, the integration of EV charging and BSS in μGs, the comparison with other optimization methods, and the emphasis on sustainability and addressing energy demand through the utilization of renewable energy and EVs.

Using the Z-network idea and conventional isolated power converters as its foundation, this article introduces a multi-port converter. This article proposes a topology that is called Multi-Port Z-Network Converter (MPZNC). A grid-connected inverter can include N input sources into a single DC bus using the suggested topology. Bypassing the input and output circuits was another capability it possessed with the boost function. Compared to traditional converters, these one-use fewer parts to integrate various energy sources. Consequently, it has better circuit properties and achieves higher conversion efficiencies. In comparison to the standard Z-Source Converter (ZSC), it improves the input-output voltage transformation ratio and provides a wider voltage control range. The circuit design, operating principle, control mechanism, and simulation data have been presented to prove technically possible. To investigate the suggested MPZNC-fed Single Phase Five Level (SPFL) inverter in the given scenario, a 1.5 kW, 230 V, 50 Hz miniature laboratory study model was created. According to the findings, the suggested converter has an efficiency of around 93% and provides double the amount of boosting time as the standard ZSC converter.

The standalone microgrids with renewable energy resources (RERs) such as a photovoltaic (PV) system and fast changing loads face major challenges in terms of reliability and power management due to a lack of inherent inertial support from RERs and their intermittent nature. Thus, energy storage technologies such as battery energy storage (BES) are typically used to mitigate the power fluctuations and maintain a power balance in the system. This paper presents a model predictive control (MPC) based power management strategy (PMS) for such standalone PV/battery systems. The proposed method is equipped with an autoregressive integrated moving average (ARIMA) prediction method to forecast the load and environmental parameters. The proposed controller has the capabilities of (1) effective power management, (2) minimization of transients during disturbances, and (3) automatic switching of the operation of the PV between the maximum power point tracking (MPPT) mode and power-curtailed mode that prevents the overcharging of the battery and at the same time maximize the PV utilization. The effectiveness of the proposed method has been verified through a comprehensive simulation-based analysis.

Integrating renewable energy sources (RES), such as photovoltaic (PV) systems, into power system networks increases uncertainty, leading to practical challenges. Therefore, an accurate photovoltaic (PV) power prediction model is required to provide essential data that supports smooth power system operation. Hence, the work presented in this paper compares and discusses the results of different machine learning (ML) techniques in predicting the power produced by the 300 MW Sakaka PV Power Plant in the north of Saudi Arabia. The validation of the presented work is performed using real-world operational data obtained from the specified solar farm. Several performance measures, including accuracy, precision, recall, F1 Score, and mean square error (MSE), are used in this work to evaluate the performance of the different ML approaches and determine the most precise prediction model. The obtained results show that the Support Vector Machine (SVM) with a Radial basis function (RBF) is the most effective approach for optimizing solar power prediction in large-scale solar farms.

Static compensators (STATCOMs) are often used in distribution systems to enhance power quality. There is a need to enhance the performance of STATCOM to optimize its utilization and facilitate the provision of additional ancillary services. This paper employs the multilayer discrete noise-eliminating second order generalized integrator (MDNESOGI) to regulate the quasi-impedance source inverter (qZSI)-STATCOM for power exchange with the grid. Compared to conventional second-order generalized integrator (SOGI), MDNESOGI exhibits a higher capability for rejecting DC offset. In instances of abnormal grid operation or system malfunction, the inclusion of DC rejection capability enhances the robustness and reliability of the system. The suggested control algorithm only requires two integrators, three mathematical operators, and a damping factor, making it far easier to implement than transformation-based methods. The distorted load current is broken down into its active and reactive components using this control mechanism. The reference currents are then calculated by multiplying these parts by their corresponding voltage standards. The DC offset is reduced and transient oscillations in the weight component are eliminated by adjusting the damping factor. The suggested algorithm effectively handles power quality tasks like (a) reducing harmonic distortion, (b) compensating for reactive power, (c) adjusting for power factor, and (d) balancing the load under different conditions in the distribution system. The experimental study results are used to examine the stability of the proposed control scheme in both static and dynamic scenarios. In addition, a comparison to traditional methods is provided to demonstrate the new method’s superiority. Experimentation results show that the suggested controller is superior to its contemporaries in all scenarios where power quality is a factor, meeting the IEEE standard requirements.